"""Warm RTF benchmark for the Bulgarian ref-audio vLLM setup."""

import asyncio
import os

from audio import LLMAudioPlayer, StreamingAudioWriter
from config import CHUNK_SIZE, LOOKBACK_FRAMES, REF_AUDIO_SECONDS, GPU_MEMORY_UTILIZATION, MAX_MODEL_LEN
from generation.vllm_generator import VLLMTTSGenerator


PROMPTS = [
    "Това е кратък тест за real-time factor на ref-audio сървъра.",
    "Рано сутрин реката е спокойна, а гласът трябва да звучи ясно, плавно и естествено.",
]


async def run_once(generator, player, prompt, reference_audio_tokens):
    audio_writer = StreamingAudioWriter(
        player,
        output_file=None,
        chunk_size=CHUNK_SIZE,
        lookback_frames=LOOKBACK_FRAMES,
    )
    audio_writer.start()
    result = await generator._generate_async(
        prompt,
        audio_writer,
        reference_audio_tokens=reference_audio_tokens,
    )
    audio_writer.finalize()
    return result


async def main():
    generator = VLLMTTSGenerator(
        tensor_parallel_size=1,
        gpu_memory_utilization=GPU_MEMORY_UTILIZATION,
        max_model_len=MAX_MODEL_LEN,
    )
    await generator.initialize_engine()

    player = LLMAudioPlayer(generator.tokenizer)
    reference_audio_path = os.environ.get("KANITTS_TEST_REF_AUDIO", "/home/nasko/besttts/REF/woman.wav")
    reference_audio_tokens, reference_frames = player.prepare_reference_audio_tokens(
        reference_audio_path=reference_audio_path,
        ref_seconds=REF_AUDIO_SECONDS,
    )

    print(f"Reference frames: {reference_frames}")
    for index, prompt in enumerate(PROMPTS, start=1):
        result = await run_once(generator, player, prompt, reference_audio_tokens)
        print(
            f"RUN {index}: tokens={len(result['all_token_ids'])} "
            f"dur={result['audio_duration']:.2f}s gen={result['generation_time']:.2f}s "
            f"RTF={result['rtf']:.3f}"
        )


if __name__ == "__main__":
    asyncio.run(main())